International Journal of Advanced Science and Technology
Vol.121 (2018), pp. 55-64
http://dx.doi.org/10.14257/ijast.2018.121.05
ISSN: 2005-4238 IJAST
Diabetes Prediction Using Artificial Neural Network
Nesreen Samer El_Jerjawi, and Samy S. Abu-Naser*
Department of Information Technology,
Faculty of Engineering & Information Technology,
Al-Azhar University - Gaza, Palestine
*Email: [email protected]
Abstract
Diabetes is one of the most common diseases worldwide where a cure is not found for it yet.
Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is
the prediction to be very accurate and to use a reliable method for that. One of these methods is
using artificial intelligence systems and in particular is the use of Artificial Neural Networks
(ANN). So in this paper, we used artificial neural networks to predict whether a person is diabetic
or not. The criterion was to minimize the error function in neural network training using a neural
network model. After training the ANN model, the average error function of the neural network
was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was
87.3%
Keywords: diabetes, neural network, ANN, prediction.
1. Introduction
Diabetes is a long-lasting disease that happens when the pancreas fails to create enough
insulin, or when the body cannot use the insulin produced efficiently. Insulin is a hormone
that controls the level of sugar in the blood. Hyperglycemia or hyperglycemia is a
common result of uncontrolled diabetes and, over time, causes severe damage to many
organs, particularly nerves and blood vessels.
In 2015, 8.5% of adults aged 17 years or older had diabetes. In 2013, diabetes was the
cause of 1.5 million deaths, and high blood glucose caused 2.3 million deaths. Diabetes
patients have doubled in the last ten years worldwide. More than 200 million people are
infected and about seven percent increase in the annual predominance of diabetes in the
world. People for a long time suffered from different diseases that in some cases have
been able to diagnose diseases and offer them the solution in order to enhance it, but
unfortunately, sometimes, due to the lack of diagnosis of symptoms in patients for a long
time may even threaten the life of the patient. Therefore, many studies have been done in
the field of predicting for several diseases to the extent that today's human take advantage
of decision supports models and smart method to predict. One of the decision support
models application is in the medical field and diagnosis of illnesses such as diabetes [1,
2]. Deferment in the diagnosis and prediction of diabetes due to insufficient control of
blood glucose increases macro vascular and Capillaries difficulties risk, ocular diseases
and kidney failure [1, 2].
So we proposed an ANN model to predict diabetes that can be useful and helpful for
doctors and practitioners. In this research, we used the following attributes: Number of
pregnancies, PG Concentration (Plasma glucose at 2 hours in an oral glucose tolerance
test), Diastolic BP (Diastolic Blood Pressure (mm Hg) )), Tri Fold Thick (Triceps Skin
Fold Thickness (mm)), Serum Ins(2-Hour Serum Insulin (mu U/ml)), BMI (Body Mass
Index: (weight in kg/ (height in m)^2) ), DP Function(Diabetes Pedigree Function), Age
(years), Diabetes (Whether or not the person has diabetes)[15].
International Journal of Advanced Science and Technology
Vol.124 (2018), pp. 1-10
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Based on the Diabetes Research Center reports, the incidence of diabetes has folded in
the last ten years worldwide and more than 200 million people are infected and about
seven percent increase in the annual prevalence of diabetes worldwide.
Since diabetes is a long-lasting disease and import permanent damage to the limbs and
vital organs in the body, using artificial intelligence tools can enhance the detection
methods and disease control which will be of a great help to the physicians. According to
the Diabetes Research Center, it has been shown that early diagnosis of patients at risk
can prevent 80 percent of lasting complications of type II diabetes or deferred them [5].
There are two types of diabetes, type I and type II diabetes. Type I diabetes also named
insulin dependent and type II diabetes named relative insulin deficiency [6]. Protracted
complications of diabetes are mainly distributed into two categories: vascular and non-
vascular complications of diabetes.
Vascular complications include micro vascular (eye disease, neuropathy,
nephropathy) and macro vascular complications (coronary artery disease, peripheral
vascular disease, cerebrovascular disease). Non-vascular complications include
gastro paresis, sexual dysfunction, and skin changes [7].
2. The objectives of the study To predict and categorize the state of health.
To identify some appropriate factors that affect health conditions,
To design an artificial neural network that can be used to predict health performance based on certain pre-defined data for a particular health condition
3. Literature review
Diabetes or diabetes mellitus is a metabolic disorder (metabolic) in the body. This
disease destroy the ability to produce insulin in the patient's body or the body develops
resistance to insulin the and consequently the produced insulin cannot achieve its normal
job. The main role of the produced insulin is to decrees blood sugar by different
instruments. There are two key types of diabetes. In Type I diabetes, obliteration of beta
pancreatic cells damage insulin construction and in type II, there is a progressive insulin
confrontation in the body and ultimately may yield to the obliteration of pancreatic beta
cells and faults in insulin production. In type II diabetes, it is known that genetic issues,
obesity and lack of physical activity have a vital part in a person [1].
Even though the precise cause of type I diabetes is unidentified, issues that may
indicate a greater risk comprise the followings [2]:
Family history. A person risk upsurges if his parent or sibling has history of type I
diabetes.
Environmental factors. Situations for example contact with a viral illness probably
play some role in type I diabetes.
The existence of harmful immune system cells. Occasionally family members of a
person with type I diabetes are examined for the existence of diabetes
autoantibodies. If a person has these autoantibodies, he/she has a chance of
increased risk for evolving type I diabetes. Nonetheless not every person who has
these autoantibodies gets diabetes.
Geography. Some countries, like Sweden, have bigger rates of type I diabetes.
Researchers don't completely comprehend why certain people develop pre-diabetes and
type II diabetes and others don't. It's sure that some factors upsurge the risk like [2]:
Weight. The more fatty tissue you have, the more resilient a person cells to insulin.
International Journal of Advanced Science and Technology
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Inactivity. The less energetic a person is, the more a person has risk. Physical
activity assists a person control of his/her weight, consumes glucose as energy and
makes a person cells more sensitive to insulin.
Family history. A person risk upsurges if his parent or sibling has history of type II
diabetes.
Race. Even though it's uncertain why, people of specific races are at higher risk.
Age. A person risk upsurges as he/she gets older. This may be because a person has
a habit to exercise less, lose muscle mass and add weight as he/she gets older.
Nonetheless type II diabetes is likewise growing among children, youths and adults.
Gestational diabetes. If a person developed gestational diabetes when she was
pregnant, her risk of emerging pre-diabetes and type II diabetes far ahead upsurges.
If she gives birth to a baby weighing more than 4 kilograms, she is also at risk of
type II diabetes.
Polycystic ovary syndrome. For females, having polycystic ovary syndrome
increases the risk of getting diabetes.
High blood pressure. Having blood pressure more than 140/90 millimeters of
mercury (mm Hg) is connected to an augmented risk of type II diabetes.
Abnormal cholesterol and triglyceride levels. If a person has low levels of high-
density lipoprotein, or good cholesterol, his/her risk of type II diabetes is going to
be higher. Triglycerides are additional type of fat passed in the blood. A person
with greater levels of triglycerides has an augmented risk of type II diabetes.
A practical approach to this type of problem is the application of regression analysis
where past data is better combined into some functions. The result is an equation in which
both xj inputs are multiplied by wj; the sum of all these products is constant, and then
output y = Σ wj xj +, where j = 0..n.
The problem is the difficulty of choosing an appropriate function to have all the
collected data and adjust the output automatically when more information is attained,
because the candidate's performance is organized by a number of arguments, and this
control will not have any clear regression model.
The artificial neural network, which emulates the human thinking in solving a problem,
is a more common approach that can address this type of problems. Thus, the attempt to
develop an adaptive system such as artificial neural network to predict the situation and
classification based on the results of these arguments [14].
3.1 Artificial Neural Network
Adaptive Artificial Neural Network is a non-parametric technique to categorize that in
the medical field based on input variables to categorize subjects into healthy or unhealthy.
Classification and prediction of the patient's condition based on risk factors are an
application of artificial neural networks [12]. Furthermore, ANN is an application of
Artificial Intelligence [19-30].
In artificial neural networks is inspired by the diverse structure of the human brain.
Billions of nerve cells (neurons) through the communication that with each other
(synapses) creates a biological neural network in the human brain that is devoted to
human activities like speaking, reading, comprehension, breathing, face detection,
movement, voice recognition, also resolve issues and data storage. Artificial neural
networks, in fact, mimic a part of brain jobs [13].
3.2 Artificial neural network structure
Neural networks are nonlinear modeling of intelligent computational methods which
recently is considered as an advance in computing and information processing tools
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acquired a significant and advanced position in the science field, and the consequences
have been promising. Feedforward neural networks are valuable type of artificial neural
networks, since feedforward neural network with a hidden layer, appropriate activation
function in the hidden layer and the sufficient hidden layer neurons are able to estimate
any function with an arbitrary accuracy. For this aim, in the following section we present
a structure of feedforward neural network modeling to prediction diabetes problem.
In general, artificial neural networks have three types of layers as follows:
Input layer: Get the raw data that has been fed to the network.
Hidden layers: the function of these layers is determined by inputs, weight, the relationship between them, and the hidden layers. Weights between input and hidden units determine when a hidden unit needs to be activated.
Output layer: output unit function depending on activity and weight of the hidden unit and the connection between hidden units and output.
3.3 The Back-propagation Training Algorithm
Initialize each wi to some small random value
Until the termination condition is met, Do
For each training example <(x1,…xn),t> Do
Input the instance (x1,…,xn) to the network and compute the network outputs
ok
For each output unit k: k=ok(1-ok)(tk-ok)
For each hidden unit h: h=oh(1-oh) k wh,k k
For each network weight wj Do
wi,j=wi,j+wi,j,where wi,j= j xi,j and is the learning rate.
3.4 Previous studies
The author in [16] used Data Mining to develop a model for classifying diabetic
patient control level based on historical medical records. The author was
motivated by the death caused by diabetes in the world which necessitated
avoiding the complication of the disease. He developed a new predictive model
using data mining techniques which would classify diabetic patient control level
based on historical medical records. The research was carried out using three
data mining techniques which are Naïve Bayes, Logistic and J48. The research
was implemented using WEKA application. The result showed that Logistic
data mining algorithm gave a precision average of 0.73, recall of 0.744, F-
measure of 0.653 and accuracy of 74.4%. Naïve Bayes gave a precision average
of 0.717, recall of 0.742, F-measure of 0.653 and accuracy of 74.2%. J48 gave a
precision average of 0.54, recall of 0.735, F-measure of 0.623 and accuracy of
73.5%. This proved that the logistic algorithm was more accurate than the other
two. The research was limited in that only diabetes type 2 was considered. They
also did not look into the discovery of appropriate features with minimal effort
and validation on discovered features.
The author in [17] developed a prediction model for diabetes Type II treatment
plans by using data mining. The author was motivated by the highly dangerous
complication of chronic disease as well as the complication which required
amputation of one of the parties. He developed a new model for classifying
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Vol.124 (2018), pp. 1-10
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diabetes type 2 treatment plans which could help the control of blood glucose
level of diabetic patient. He made use of J48 algorithm in conducting the
experiment on 318 medical records which was collected from JABER ABN
ABU ALIZ clinic center for diabetes in Sudan. The basic control information
showed that 59.1% of the record was considered for Oral Hypoglycemic, 35.5%
for Insulin and 5.3% for Diet. The evaluation was done using the WEKA
application. The research work did not consider diabetes type 1 patients which
could have been included with additional attributes. Also, the nutrition system
and exercise could have been included to increase the accuracy of the system.
The authors in [18] used prediction of diabetes mellitus based on boosting
ensemble modeling. They were motivated by the focus of aiding diabetes
patients fit themselves into their normal activities of life by early predicting their
state and tacking it. They intended to predict the diabetes types of patients based
on physical and clinical information using boosting ensemble technique. They
made use of boosting ensemble technique which internally uses random
committee classifier. The architecture used was supported by integrating data
management, learning, and prediction components together. The evaluation
result of the technique showed accuracy gave a weighted average TP rate of
0.81, FP rate of 0.198, Precision of 0.81, Recall of 0.81, F-measure of 0.82 and
ROC area of 0.82 for diabetes type 1 and 2. The research work is intended to be
extended in future the integration into a cloud based clinical decision support
system for chronic diseases and the inclusion of a feedback mechanism to
increase the level of satisfaction of users.
Sernyak used logistic regression analysis to calculate odds ratio neuroleptic
unusual version and a diagnosis of diabetes in each of the age groups, control
the effects of population, and diagnosis [9]. Thirugnanam has improved diabetes
prediction using fuzzy neural networks [10]. Hamid and others have offered
hybrid intelligent systems for the detection of micro albuminuria in patients with
type 2 diabetes without measuring the urinary albumin [11]. Javad and others
proposed the method base on automatic learning on type II diabetes to regulate
blood sugar [12].
4. Methodology
By looking intensely through literature and soliciting the experience of human experts
on pathological conditions, a number of factors have been recognized that have an impact
on determining patients' cases in the subsequent period. These factors were prudently
studied and coordinated with an appropriate number for coding the computer within the
modeling environment ANN. These factors were categorized as input variables and output
variables that reflect some possible levels of disease status in terms of the assessment
system. The data were entered into the JNN tool environment, determined the value of
each of the variables using JNN(the most influential factor on diabetes), then the data
were trained, validated, and tested.
4.1 Input variables
The specified input variables are those that can be obtained simply from the file system
and the registry of diseases. Input variables are:
Table 1: attributes in the Data set
No. Attribute name
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1 Pregnancies: Number of pregnancies
2 PG Concentration: Plasma glucose at 2 hours in an oral glucose tolerance
test
3 Diastolic BP: Diastolic Blood Pressure (mm Hg)
4 Tri Fold Thick: Triceps Skin Fold Thickness (mm)
5 Serum Ins: 2-Hour Serum Insulin (mu U/ml)
6 BMI: Body Mass Index: (weight in kg/ (height in m)^2)
7 DP Function: Diabetes Pedigree Function
8 Age: Age (years)
9 Diabetes: Whether or not the person diabetes
These factors were converted into a format suitable for neural network analysis as
shown in Table2 "data set up to 1004", Input characteristics 8 and one output (0 diabetic,
1 healthy)
Table 2. Input Data Transformation
S/N Input
variable Domain S/N
Input
variable Domain
1
Pregnancies Number of
pregnancies
H
S
6
BMI Body Mass
Index: (weight
in kg/ (height in
m)^2)
2
1
2
PG
Concentrati
on
Plasma glucose
at 2 hours in an
oral glucose
tolerance test
H
S
7
DP
Function
Diabetes
Pedigree
Function
0
1
3
Diastolic
BP
Diastolic Blood
Pressure (mm
Hg)
H
S
8
Age
Age (years)
0
1
4
Tri Fold
Thick:
Triceps Skin
Fold Thickness
(mm)
H
S
9
Diabetes Whether or not
the person has
diabetes
0
1
5
Serum)
Ins2-Hour
Serum Insulin
(mu U/ml
H
S
4.2 The Output Variable
The output variable represents whether a person has diabetes or not (Sick, Healthy).
Table 3: Output Data Transformation
S/N Output
Variable Diabetes
1 Healthy "1 " The person does not have diabetes
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2 Sick "0" The person has diabetes
Table 3 shows the classification of the selected output variable, which is consistent
with the classification system, in the identification of disease cases.
4.3. Neural network evaluation
As mentioned above, the purpose of this experiment was to identify whether or not the
person has diabetes. We used Backpropagation algorithm, which provides the ability to
perform neural network learning and testing. Our neural network is the front feed
network, with one input layer (8 inputs), 3 hidden layers and one output layer (1 output)
as seen in Figure 2.
The proposed model is implemented in Just Neural Network (JNN) environment. The
dataset for the diagnoses of diabetes were gathered from the documentation of the
Association of diabetic’s city of Urmia which contains 1004 samples with 9 attributes (as
seen in Fig 1). This model was used to determine the value of each of the variables using
JNN which they are the most influential factor on diabetes prediction as shown in figure
3. After training and validating, the network, it was tested using the test data and the
following results were obtained.
The accuracy of the diabetes predication was (87.3%). The average error was 0.010.
The training cycles (number of epochs) were 158,000. The training examples were 767.
The number of validating examples was 237 as seen in figure 4.
Figure 1: Attribute inputs
International Journal of Advanced Science and Technology
Vol.124 (2018), pp. 1-10
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Figure 2: Artificial Neural Network Structure
Figure 3: Attributes importance (the most influential factor on diabetes)
Figure 4: Learning progress
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5 Conclusion
In this paper, artificial neural network was used to predict diabetes. Using artificial
neural networks model we can design and implement complex medical processes using
software. The software systems are more effective and efficient in various medical fields
including predicting, diagnosing, treating and helping the surgeons, physicians, and the
general population. These systems can be implemented in a parallel way and are
distributed in different measures. In general, artificial neural network is a parallel
processing system that is used to detect complex patterns in the data. The aim of this
study was to determine the effective variables and their impact on diabetes. The proposed
model was implemented in JNN environment.
The diabetes dataset contains 1004 samples with 9 attributes. This model was first used
to determine the value of each of the variables using JNN (the most influential factor on
diabetes). After training, validating, and testing the dataset, we got (87.3%) accuracy,
average error was (0.010), number of epochs was (158,000), number of training examples
was (767), and number of validating examples was (237).
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DISEASES USING CLIPS. Journal of Theoretical & Applied Information Technology, 4(10).
[49] Bakeer, H., & Abu-Naser, S. S. (2017). Photo Copier Maintenance Expert System V. 01 Using SL5
Object Language. International Journal of Engineering and Information Systems (IJEAIS) 1 (4), 116-
124.
[50] Abu Naser, S. S., & Shaath, M. Z. (2016). Expert system urination problems diagnosis. World Wide
Journal of Multidisciplinary Research and Development, 2(5), 9-19.
[51] Abu-Naser, S. S., El-Hissi, H., Abu-Rass, M., & El-Khozondar, N. (2010). An expert system for
endocrine diagnosis and treatments using JESS. Journal of Artificial Intelligence; Scialert, 3(4), 239-
251.
[52] Mrouf, A., Albatish, I., Mosa, M., & Abu Naser, S. S. (2017). Knowledge Based System for Long-
term Abdominal Pain (Stomach Pain) Diagnosis and Treatment. International Journal of Engineering
and Information Systems (IJEAIS) 1 (4), 71-88.
[53] Abu Naser, S. S., Baraka, M. H.,& Baraka, A. R. (2008). A Proposed Expert System For Guiding
Freshman Students In Selecting A Major In Al-Azhar University, Gaza.Journal of Theoretical &
Applied Information Technology 4(9).
[54] Abu-Nasser, B. S., & Abu-Naser, S. S. (2018). Cognitive System for Helping Farmers in Diagnosing
Watermelon Diseases. International Journal of Academic Information Systems Research (IJAISR) 2
(7), 1-7.
[55] Abu Naser, S. S., Alamawi, W. W., & Alfarra, M. F. (2016). Rule Based System for Diagnosing
Wireless Connection Problems Using SL5 Object. International Journal of Information Technology
and Electrical Engineering 5(6), 26-33.
[56] Akkila, A. N., & Abu Naser, S. S. (2016). Proposed Expert System for Calculating Inheritance in
Islam. World Wide Journal of Multidisciplinary Research and Development 2 (9), 38-48.
[57] Abu Naser, S. S., & Zaqout, I. S. (2016). Knowledge-based systems that determine the appropriate
students major: In the faculty of engineering and information technology, World Wide Journal of
Multidisciplinary Research and Development 2 (10), 26-34.
[58] Abu Naser, S. S., & Bastami, B. G. (2016). A proposed rule based system for breasts cancer
diagnosis. World Wide Journal of Multidisciplinary Research and Development 2 (5), 27-33.
[59] Abu-Nasser, B. S. (2017). Medical Expert Systems Survey. International Journal of Engineering and
Information Systems, 1(7), 218-224.
[60] Abu Naser, S. S., & ALmursheidi, S. H. (2016). A Knowledge Based System for Neck Pain
Diagnosis. World Wide Journal of Multidisciplinary Research and Development (WWJMRD), 2(4),
12-18.
[61] Azaab, S., Abu Naser, S., & Sulisel, O. (2000). A proposed expert system for selecting exploratory
factor analysis procedures. Journal of the College of Education 4 (2), 9-26.
[62] Abu-Naser, S. S., Kashkash, K. A., & Fayyad, M. (2010). Developing an expert system for plant
disease diagnosis. Journal of Artificial Intelligence, 3 (4), 269-276.
[63] Barhoom, A. M., & Abu-Naser, S. S. (2018). Black Pepper Expert System. International Journal of
Academic Information Systems Research, (IJAISR) 2 (8), 9-16.
[64] AlZamily, J. Y., & Abu-Naser, S. S. (2018). A Cognitive System for Diagnosing Musa Acuminata
Disorders. International Journal of Academic Information Systems Research, (IJAISR) 2 (8), 1-8.
[65] Alajrami, M. A., & Abu-Naser, S. S. (2018). Onion Rule Based System for Disorders Diagnosis and
Treatment. International Journal of Academic Pedagogical Research (IJAPR), 2 (8), 1-9.
[66] Salah, M., Altalla, K., Salah, A., & Abu-Naser, S. S. (2018). Predicting Medical Expenses Using
Artificial Neural Network. International Journal of Engineering and Information Systems (IJEAIS),
2(20), 11-17.
[67] Marouf, A., & Abu-Naser, S. S. (2018). Predicting Antibiotic Susceptibility Using Artificial Neural
Network. International Journal of Academic Pedagogical Research (IJAPR), 2(10), 1-5.
[68] Jamala, M. N., & Abu-Naser, S. S. (2018). Predicting MPG for Automobile Using Artificial Neural
Network Analysis. International Journal of Academic Information Systems Research (IJAISR), 2(10),
5-21.
[69] Kashf, D. W. A., Okasha, A. N., Sahyoun, N. A., El-Rabi, R. E., & Abu-Naser, S. S. (2018).
Predicting DNA Lung Cancer using Artificial Neural Network. International Journal of Academic
Pedagogical Research (IJAPR), 2(10), 6-13.
[70] Al-Massri, R. Y., Al-Astel, Y., Ziadia, H., Mousa, D. K., & Abu-Naser, S. S. (2018). Classification
Prediction of SBRCTs Cancers Using Artificial Neural Network. International Journal of Academic
Engineering Research (IJAER), 2(11), 1-7.
[71] Alghoul, A., Al Ajrami, S., Al Jarousha, G., Harb, G., & Abu-Naser, S. S. (2018). Email
Classification Using Artificial Neural Network. International Journal of Academic Engineering
Research (IJAER), 2(11), 8-14.
International Journal of Advanced Science and Technology
Vol.124 (2018), pp. 1-10
12
[72] Metwally, N. F., AbuSharekh, E. K., & Abu-Naser, S. S. (2018). Diagnosis of Hepatitis Virus Using
Artificial Neural Network. International Journal of Academic Pedagogical Research (IJAPR), 2(11),
1-7.
[73] Heriz, H. H., Salah, H. M., Abu Abdu, S. B., El Sbihi, M. M., & Abu-Naser, S. S. (2018). English
Alphabet Prediction Using Artificial Neural Networks. International Journal of Academic
Pedagogical Research (IJAPR), 2(11), 8-14.
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